Logistic Regression using Sklearn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pydataset import data\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "df = pd.read_csv('insurance_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bought_insurance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  bought_insurance\n",
       "0    22                 0\n",
       "1    25                 0\n",
       "2    47                 1\n",
       "3    52                 0\n",
       "4    46                 1\n",
       "5    56                 1\n",
       "6    55                 0\n",
       "7    60                 1\n",
       "8    62                 1\n",
       "9    61                 1\n",
       "10   18                 0\n",
       "11   28                 0\n",
       "12   27                 0\n",
       "13   29                 0\n",
       "14   49                 1\n",
       "15   55                 1\n",
       "16   25                 1\n",
       "17   58                 1\n",
       "18   19                 0\n",
       "19   18                 0\n",
       "20   21                 0\n",
       "21   26                 0\n",
       "22   40                 1\n",
       "23   45                 1\n",
       "24   50                 1\n",
       "25   54                 1\n",
       "26   23                 0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2497d646518>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qJ5jiOUny2CSPP38b+CHg00zxY6eq/h04k+S7u00vBI6zjuek2TcxJfl+4GPAp/jmtdRfoXfd/SCwDfg88LKq+spEmlxjSZ5N70WfDfSe2A9W1c1Jnk5v1fok4D7gFVX19cl1OhlJdgO/WFUvmeY56b73D3bDjcD7quq3kjyZKX3sACS5kt6L7pcBp4FX0T2OWIdz0my4S9I0a/ayjCRNM8NdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QG/T82voM8hM3khQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.age, df.bought_insurance, marker='+', color='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(df[['age']], df.bought_insurance, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_train, Y_train, marker='+', color='b', alpha=0.7, label='Train Data')\n",
    "plt.scatter(X_test, Y_test, marker='o', color='r', alpha=0.7, label='Test Data')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91961\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAF6tJREFUeJzt3X101NWdx/H3VxBZA0jlwaMESDyiBEICMqDlQY3tWlCKWw9s4ZQu9ahoz7Lb7UJX6Fa7S88eqx4tpWC7nOrKWhV8rLRSpVYNolUJipYHKYFQiXFNRHlIKE/y3T9miEmYZIZkkmHufF7nzGF+v3vzm+/vjvPJ9WbmN+buiIhIWE5LdwEiIpJ6CncRkQAp3EVEAqRwFxEJkMJdRCRACncRkQAp3EVEAqRwFxEJkMJdRCRAndP1wL179/a8vLx0PbyISEZav379x+7eJ1G/tIV7Xl4eZWVl6Xp4EZGMZGZ/SaaflmVERAKkcBcRCZDCXUQkQGlbc4/nyJEjVFZWcvDgwXSXEryuXbuSm5vL6aefnu5SRKQdnFLhXllZSffu3cnLy8PM0l1OsNyd3bt3U1lZSX5+frrLEZF2cEotyxw8eJBevXop2NuZmdGrVy/9H5JIwE6pcAcU7B1E4ywStlMu3EVEpO0ShruZPWBm1Wa2sZl2M7NFZlZuZu+a2cWpL7Nj7Nmzh/vuu6/VP79w4UIOHDiQsN/LL7/MpEmTWuyzYcMGVq1a1epaJIOVlvLu4KlUnhuBqVOhtDTdFbW/ls65tDS6LxK/rcPGqrWPlabnM5mZ+4PAhBbaJwKDYrdZwM/bXlZ6dFS4J0PhnqVKS2HOHLrVVbP/zHOguhrmzAk74Fs651gb1dVwTvy2Dhmr1j5WGp/PhOHu7muAT1roci3wvx71OtDTzM5NVYGJzJ8fvaXCvHnz2L59O8OHD+d73/seAHfffTejRo2iqKiIH/7whwDU1dVxzTXXUFxcTGFhIStWrGDRokVUVVVRUlJCSUnJCcd+7rnnGDx4MOPGjeOpp56q3//mm28yZswYRowYwZgxY9i6dSuHDx/m9ttvZ8WKFQwfPpwVK1bE7Sfheffmxez4KIePD/dgf+1pbPmgBzs+yoHFi9NdWrtp8ZwXL4acHOjRA047LfpvTrStI8eqtY+VzuczFW+F7AfsarBdGdv3YdOOZjaL6OyeAQMGpOChU+vHP/4xGzduZMOGDQCsXr2abdu28eabb+LuTJ48mTVr1lBTU8N5553Hs88+C8DevXs566yzuPfee3nppZfo3bt3o+MePHiQm266iRdffJELLriAr3/96/VtgwcPZs2aNXTu3JkXXniB73//+zz55JMsWLCAsrIyFsf+I9i3b1/cfhKWs/dWRGd4hz/fd6hLN6ioSF9R7SzhOZ9zTuMf6BZtO3svHTZWrX1e0vl8piLc473twuN1dPelwFKASCQSt0+yjs/WN25svH3HHW05amOrV69m9erVjBgxAoDa2lq2bdvG+PHjmTt3LrfeeiuTJk1i/PjxLR7nvffeIz8/n0GDBgEwY8YMli5dCkR/McycOZNt27ZhZhw5ciTuMZLtJ5ktd1w+VFez5YMeABQUAPtqoW+4n0dIeM7V1dEZ+3G1tZCfT24+HTZWrX1e0vl8puLdMpVA/wbbuUBVCo6bdu7O/Pnz2bBhAxs2bKC8vJwbbriBCy+8kPXr1zNs2DDmz5/PggULEh6rubce3nbbbZSUlLBx40Z+85vfNPve82T7SYabPRvq6jjj0D7wY7BvH9TVRfeHqqVzjrWxbx8ci9/WIWPV2sdK4/OZinBfCfxD7F0zlwJ73f2EJZlUu+OO6K2wMHo7vt0W3bt3Z//+/fXbX/nKV3jggQeora0F4IMPPqC6upqqqirOPPNMZsyYwdy5c3nrrbfi/vxxgwcPpqKigu3btwPw6KOP1rft3buXfv36AfDggw82W0tz/SQwl18O99zD+Zf2peALH0HfvnDPPdH9oWrpnGNt9O0LH8Vv65Cxau1jpfP5dPcWb8CjRNfPjxCdpd8A3ALcEms3YAmwHfgTEEl0THdn5MiR3tTmzZtP2JfIvHnRW6pMnz7dhw4d6nPnznV394ULF3phYaEXFhb6pZde6uXl5f7cc8/5sGHDvLi42CORiK9bt87d3RctWuQXXXSRX3HFFScc93e/+51fdNFFPnbsWL/11lv9mmuucXf31157zQcNGuRjxozxH/zgBz5w4EB3d9+9e7dHIhEvLi725cuXN9uvLVoz3iKSXkCZJ5GxFu3b8SKRiDf9so4tW7ZQUFCQlnqykcZbJPOY2Xp3jyTqp0+oiogESOEuIhIghbuISIAU7iIiAVK4i4gESOEuIhIghXs769atGwBVVVVMmTKlxb5Nryp59dVXs2fPnnatT0TCpHBvhc8+++ykf+a8887jiSeeaLFP03BftWoVPXv2POnHEhHJ7HBv6SL+rbRz504GDx7MzJkzKSoqYsqUKRw4cIC8vDwWLFjAuHHjePzxx9m+fTsTJkxg5MiRjB8/nvfeew+AiooKvvjFLzJq1Chuu+22RsctLCwEor8c5s6dy7BhwygqKuJnP/tZ3EsG5+Xl8fHHHwNw7733UlhYSGFhIQsXLqw/ZkFBATfddBNDhw7lqquu4q9//SsAixYtYsiQIRQVFTFt2rQ2j4uIZJhkPsbaHrc2X37g5ZfdR450v+wy96uvjv47cmR0fxtUVFQ44GvXrnV39+uvv97vvvtuHzhwoN955531/a688kr/85//7O7ur7/+upeUlLi7+1e/+lVftmyZu7svXrzYc3Jy6o87dOhQd3e/7777/LrrrvMjR464e/RSA+7uAwcO9JqamvrHOL5dVlbmhYWFXltb6/v37/chQ4b4W2+95RUVFd6pUyd/++233d196tSp/tBDD7m7+7nnnusHDx50d/dPP/007rnq8gMimYckLz+QuTP3Fi7i31b9+/dn7NixQPTyvGvXrgWovw57bW0tr732GlOnTmX48OHcfPPNfPhh9Fppr776KtOnTwfgm9/8Ztzjv/DCC9xyyy107hy94vLZZ5/dYj1r167la1/7Gjk5OXTr1o3rrruOV155BYD8/HyGDx8OwMiRI9m5cycARUVFfOMb3+BXv/pV/eOISPbI3Fd9RUWzF/Fvq6aX5z2+nZOTA8CxY8fo2bNn/Zd6JPr5ptw9YZ+m/Ztzxhln1N/v1KlT/bLMs88+y5o1a1i5ciU/+tGP2LRpk0JeJItk7sw9Pz960f6GYhfxb6v333+fP/7xj0D08rzjxo1r1N6jRw/y8/N5/PHHgWj4vvPOOwCMHTuW5cuXA/Dwww/HPf5VV13FL37xC44ePQrAJ59Ev8WwuUsGX3bZZfz617/mwIED1NXV8fTTT7f4BSHHjh1j165dlJSUcNddd7Fnz576yxaLSHbI3HBv6SL+bVRQUMCyZcsoKirik08+4dvf/vYJfR5++GHuv/9+iouLGTp0KM888wwAP/3pT1myZAmjRo1i7969cY9/4403MmDAAIqKiiguLuaRRx4BYNasWUycOPGE72C9+OKL+da3vsXo0aO55JJLuPHGG+u/HSqezz77jBkzZjBs2DBGjBjBd7/7Xb3rRiTLZPYlf0tLo2vsFRXRGfvs2W2+CP7OnTuZNGkSG49/f1/AdMlfkcyT7CV/M3sR9vi3sYiISCOZuyzTTvLy8rJi1i4iYTvlwj1dy0TZRuMsErZTKty7du3K7t27FTztzN3ZvXs3Xbt2TXcpItJOTqk199zcXCorK6mpqUl3KcHr2rUrubm56S5DRNrJKRXup59+OvkpeJ+6iEi2O6WWZUREJDUU7iIiAVK4i4gESOEuIhIghbuISIAU7iIiAVK4i4gESOEuIhKgpMLdzCaY2VYzKzezeXHaB5jZS2b2tpm9a2ZXp75UERFJVsJwN7NOwBJgIjAEmG5mQ5p0+wHwmLuPAKYB96W6UBERSV4yM/fRQLm773D3w8By4NomfRzoEbt/FlCVuhJFRORkJXNtmX7ArgbblcAlTfr8B7DazP4JyAG+nJLqRESkVZKZuVucfU2vyTsdeNDdc4GrgYfM7IRjm9ksMyszszJd+VFEpP0kE+6VQP8G27mcuOxyA/AYgLv/EegK9G56IHdf6u4Rd4/06dOndRWLiEhCyYT7OmCQmeWbWReifzBd2aTP+8CXAMysgGi4a2ouIpImCcPd3Y8Cs4HngS1E3xWzycwWmNnkWLc5wE1m9g7wKPAt19cpiYikTVJf1uHuq4BVTfbd3uD+ZmBsaksTEZHW0idURUQCpHAXEQmQwl1EJEAKdxGRACncRUQCpHAXEQmQwl1EJEAKdxGRACncRUQCpHAXEQmQwl1EJEAKdxGRACncRUQCpHAXEQmQwl1EJEAKdxGRACncRUQCpHAXEQmQwl1EJEAKdxGRACncRUQCpHAXEQmQwl1EJEAKdxGRACncRUQCpHAXEQmQwl1EJEAKdxGRACncRUQClFS4m9kEM9tqZuVmNq+ZPn9vZpvNbJOZPZLaMkVE5GR0TtTBzDoBS4C/BSqBdWa20t03N+gzCJgPjHX3T82sb3sVLCIiiSUzcx8NlLv7Dnc/DCwHrm3S5yZgibt/CuDu1aktU0RETkYy4d4P2NVguzK2r6ELgQvN7FUze93MJqSqQBEROXkJl2UAi7PP4xxnEHAFkAu8YmaF7r6n0YHMZgGzAAYMGHDSxYqISHKSmblXAv0bbOcCVXH6POPuR9y9AthKNOwbcfel7h5x90ifPn1aW7OIiCSQTLivAwaZWb6ZdQGmASub9Pk1UAJgZr2JLtPsSGWhIiKSvITh7u5HgdnA88AW4DF332RmC8xscqzb88BuM9sMvAR8z913t1fRIiLSMnNvunzeMSKRiJeVlaXlsUVEMpWZrXf3SKJ++oSqiEiAFO4iIgFSuIuIBEjhLiISIIW7iEiAFO4iIgFSuIuIBEjhLiISIIW7iEiAFO4iIgFSuIuIBEjhLiISIIW7iEiAFO4iIgFSuIuIBEjhLiISIIW7iEiAFO4iIgFSuIuIBEjhLiISIIW7iEiAFO4iIgFSuIuIBEjhLiISIIW7iEiAFO4iIgFSuIuIBEjhLiISIIW7iEiAFO4iIgFKKtzNbIKZbTWzcjOb10K/KWbmZhZJXYkiInKyEoa7mXUClgATgSHAdDMbEqdfd+CfgTdSXaSIiJycZGbuo4Fyd9/h7oeB5cC1cfr9CLgLOJjC+kREpBWSCfd+wK4G25WxffXMbATQ391/29KBzGyWmZWZWVlNTc1JFysiIslJJtwtzj6vbzQ7DfgJMCfRgdx9qbtH3D3Sp0+f5KsUEZGTkky4VwL9G2znAlUNtrsDhcDLZrYTuBRYqT+qioikTzLhvg4YZGb5ZtYFmAasPN7o7nvdvbe757l7HvA6MNndy9qlYhERSShhuLv7UWA28DywBXjM3TeZ2QIzm9zeBYqIyMnrnEwnd18FrGqy7/Zm+l7R9rJERKQt9AlVEZEAKdxFRAKkcBcRCZDCXUQkQAp3EZEAKdxFRAKkcBcRCZDCXUQkQAp3EZEAKdxFRAKkcBcRCZDCXUQkQAp3EZEAKdxFRAKkcBcRCZDCXUQkQAp3EZEAKdxFRAKkcBcRCZDCXUQkQAp3EZEAKdxFRAKkcBcRCZDCXUQkQAp3EZEAKdxFRAKkcBcRCZDCXUQkQAp3EZEAJRXuZjbBzLaaWbmZzYvT/q9mttnM3jWzP5jZwNSXKiIiyUoY7mbWCVgCTASGANPNbEiTbm8DEXcvAp4A7kp1oSIikrxkZu6jgXJ33+Huh4HlwLUNO7j7S+5+ILb5OpCb2jJFRORkJBPu/YBdDbYrY/uacwPwu7YUJSIibdM5iT4WZ5/H7Wg2A4gAlzfTPguYBTBgwIAkSxQRkZOVzMy9EujfYDsXqGraycy+DPw7MNndD8U7kLsvdfeIu0f69OnTmnpFRCQJyYT7OmCQmeWbWRdgGrCyYQczGwH8N9Fgr059mSIicjIShru7HwVmA88DW4DH3H2TmS0ws8mxbncD3YDHzWyDma1s5nAiItIBkllzx91XAaua7Lu9wf0vp7guERFpA31CVUQkQAp3EZEAKdxFRAKkcBcRCZDCXUQkQAp3EZEAKdxFRAKkcBcRCZDCXUQkQAp3EZEAKdxFRAKkcBcRCZDCXUQkQAp3EZEAKdxFRAKkcBcRCZDCXUQkQAp3EZEAKdxFRAKkcBcRCZDCXUQkQAp3EZEAKdxFRAKkcBcRCZDCXUQkQAp3EZEAKdxFRAKkcBcRCZDCXUQkQEmFu5lNMLOtZlZuZvPitJ9hZiti7W+YWV6qCxURkeQlDHcz6wQsASYCQ4DpZjakSbcbgE/d/QLgJ8CdqS70BKWlvDt4KpXnRmDqVCgtTa4tk4V6XiIJzJ8fvUnykpm5jwbK3X2Hux8GlgPXNulzLbAsdv8J4EtmZqkrs4nSUpgzh2511ew/8xyoroY5c6L7W2rLZKGel4i0i85J9OkH7GqwXQlc0lwfdz9qZnuBXsDHqSiyqXdvXky3uhw+PtwDDsOWD3pwxiE4f/Fi3v0TzbZx+eXtUU6HaOmcM/m8RFpyfLa+cWPj7TvuSE89mSSZmXu8Gbi3og9mNsvMysysrKamJpn64jp7bwWHunRrtO9Ql25QUdFiWyYL9bxEpH0kM3OvBPo32M4FqprpU2lmnYGzgE+aHsjdlwJLASKRyAnhn6zccflQXc2WD3oAUFAA7KuFvvnk5tNsWyZr6ZxFQnV8hq4Z+8lLZua+DhhkZvlm1gWYBqxs0mclMDN2fwrworu3OrwTmj0b6uo449A+8GOwbx/U1UX3t9SWyUI9LxFpF5ZMBpvZ1cBCoBPwgLv/l5ktAMrcfaWZdQUeAkYQnbFPc/cdLR0zEol4WVlZ6ysvLYXFi6PLEvn50ZA7vvbcUlsmC/W8RCRpZrbe3SMJ+7XnBLslbQ53EZEslGy46xOqIiIBUriLiARI4S4iEiCFu4hIgBTuIiIBUriLiARI4S4iEiCFu4hIgNL2ISYzqwH+kpYHj+pNO121MkNpPD6nsWhM49FYusdjoLv3SdQpbeGebmZWlsynvLKFxuNzGovGNB6NZcp4aFlGRCRACncRkQBlc7gvTXcBpxiNx+c0Fo1pPBrLiPHI2jV3EZGQZfPMXUQkWMGHu5n1N7OXzGyLmW0ys+/E9p9tZr83s22xf7+Q7lo7gpl1NbM3zeyd2Hj8Z2x/vpm9ERuPFbFv3coaZtbJzN42s9/GtrN2PMxsp5n9ycw2mFlZbF9Wvl4AzKynmT1hZu/FcuSLmTAewYc7cBSY4+4FwKXAP5rZEGAe8Ad3HwT8IbadDQ4BV7p7MTAcmGBmlwJ3Aj+JjcenwA1prDEdvgNsabCd7eNR4u7DG7zlL1tfLwA/BZ5z98FAMdH/Tk758Qg+3N39Q3d/K3Z/P9Enph9wLbAs1m0Z8HfpqbBjeVRtbPP02M2BK4EnYvuzZjwAzCwXuAb4ZWzbyOLxaEZWvl7MrAdwGXA/gLsfdvc9ZMB4BB/uDZlZHtHveX0DOMfdP4ToLwCgb/oq61ixJYgNQDXwe2A7sMfdj8a6VBL9BZgtFgL/BhyLbfciu8fDgdVmtt7MZsX2Zevr5XygBvif2LLdL80shwwYj6wJdzPrBjwJ/Iu770t3Penk7p+5+3AgFxgNFMTr1rFVpYeZTQKq3X19w91xumbFeMSMdfeLgYlElzEvS3dBadQZuBj4ubuPAOo4BZdg4smKcDez04kG+8Pu/lRs90dmdm6s/Vyis9isEvvfy5eJ/i2ip5l1jjXlAlXpqquDjQUmm9lOYDnR5ZiFZO944O5VsX+rgaeJTgCy9fVSCVS6+xux7SeIhv0pPx7Bh3ts/fR+YIu739ugaSUwM3Z/JvBMR9eWDmbWx8x6xu7/DfBlon+HeAmYEuuWNePh7vPdPdfd84BpwIvu/g2ydDzMLMfMuh+/D1wFbCRLXy/u/n/ALjO7KLbrS8BmMmA8gv8Qk5mNA14B/sTna6rfJ7ru/hgwAHgfmOrun6SlyA5kZkVE/wDUiegv98fcfYGZnU905no28DYww90Ppa/SjmdmVwBz3X1Sto5H7Lyfjm12Bh5x9/8ys15k4esFwMyGE/1jexdgB3A9sdcOp/B4BB/uIiLZKPhlGRGRbKRwFxEJkMJdRCRACncRkQAp3EVEAqRwFxEJkMJdRCRACncRkQD9P1SRuV5zR9V7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction = model.predict(X_test)\n",
    "plt.scatter(X_test, Y_test, marker='+', color='b', alpha=0.7, label='test data')\n",
    "plt.scatter(X_test, prediction, marker='o', color='r', alpha=0.7, label='predictions')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 0, 0, 1, 1, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8888888888888888"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Accuracy\n",
    "model.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8888888888888888"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.22387771, 0.77612229],\n",
       "       [0.23227079, 0.76772921],\n",
       "       [0.32766526, 0.67233474],\n",
       "       [0.6384201 , 0.3615799 ],\n",
       "       [0.53479891, 0.46520109],\n",
       "       [0.24088086, 0.75911914],\n",
       "       [0.19248897, 0.80751103],\n",
       "       [0.62734336, 0.37265664],\n",
       "       [0.17810026, 0.82189974]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(X_test) # Probablity of buying the insurance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 0, 0, 1, 1, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.55842561, 0.44157439]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(np.array([[25]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}